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OpenAlex Citation Counts

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OpenAlex is a bibliographic catalogue of scientific papers, authors and institutions accessible in open access mode, named after the Library of Alexandria. It's citation coverage is excellent and I hope you will find utility in this listing of citing articles!

If you click the article title, you'll navigate to the article, as listed in CrossRef. If you click the Open Access links, you'll navigate to the "best Open Access location". Clicking the citation count will open this listing for that article. Lastly at the bottom of the page, you'll find basic pagination options.

Requested Article:

Machine learning models development for shear strength prediction of reinforced concrete beam: a comparative study
Zaher Mundher Yaseen‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬
Scientific Reports (2023) Vol. 13, Iss. 1
Open Access | Times Cited: 18

Showing 18 citing articles:

Machine-Learning Methods for Estimating Performance of Structural Concrete Members Reinforced with Fiber-Reinforced Polymers
Farzin Kazemi, Neda Asgarkhani, Torkan Shafighfard, et al.
Archives of Computational Methods in Engineering (2024)
Open Access | Times Cited: 30
Efficient machine learning algorithm with enhanced cat swarm optimization for prediction of compressive strength of GGBS-based geopolymer concrete at elevated temperature
Pankaj Kumar Dash, Suraj Kumar Parhi, Sanjaya Kumar Patro, et al.
Construction and Building Materials (2023) Vol. 400, pp. 132814-132814
Closed Access | Times Cited: 29
Design optimization of solar collectors with hybrid nanofluids: An integrated ansys and machine learning study
Omer A. Alawi, Haslinda Mohamed Kamar, Ali H. Abdelrazek, et al.
Solar Energy Materials and Solar Cells (2024) Vol. 271, pp. 112822-112822
Closed Access | Times Cited: 7
Data driven insights for parabolic trough solar collectors: Artificial intelligence-based energy and exergy performance analysis
Tao Hai, Omer A. Alawi, Raad Z. Homod, et al.
Journal of Cleaner Production (2024) Vol. 443, pp. 141069-141069
Closed Access | Times Cited: 6
Moment–Curvature Relationship Prediction of Reinforced Concrete Beams by Adaptive Neuro-Fuzzy Inference System (ANFIS) Modelling
Atakan Mangır, Vefa Okumuş, Zekâi̇ Şen
International Journal of Fuzzy Systems (2025)
Open Access
Eco-Friendly Design and Sustainability Assessments of Fibre-Reinforced High-Strength Concrete Structures Automated by Data-Driven Machine Learning Models
Xia Qin, Sakdirat Kaewunruen
Sustainability (2023) Vol. 15, Iss. 8, pp. 6640-6640
Open Access | Times Cited: 10
Unveiling the SALSTM-M5T model and its python implementation for precise solar radiation prediction
Mohammad Ehteram, Hanieh Shabanian
Energy Reports (2023) Vol. 10, pp. 3402-3417
Open Access | Times Cited: 8
Machine learning prediction of concrete frost resistance and optimization design of mix proportions
Jinpeng Dai, Zhijie Zhang, Xiaoyuan Yang, et al.
Journal of Intelligent & Fuzzy Systems (2024), pp. 1-26
Closed Access | Times Cited: 2
Predicting split tensile strength in Portland and geopolymer concretes using machine learning algorithms: a comparative study
Rajesh Kumar Paswan, Abhilash Gogineni, Sanjay Sharma, et al.
Journal of Building Pathology and Rehabilitation (2024) Vol. 9, Iss. 2
Closed Access | Times Cited: 1
Prediction of frost resistance and multiobjective optimisation of low-carbon concrete on the basis of machine learning
Jinpeng Dai, Zhijie Zhang, Xuwei Dong, et al.
Materials Today Communications (2024) Vol. 40, pp. 109525-109525
Closed Access | Times Cited: 1
The Efficiency of Using Machine Learning Techniques in Fiber-Reinforced-Polymer Applications in Structural Engineering
Mohammad Alhusban, Mohannad Alhusban, Ayah A. Alkhawaldeh
Sustainability (2023) Vol. 16, Iss. 1, pp. 11-11
Open Access | Times Cited: 3
Predicting shear modulus property using materials informatics
M. Dharani, Malavika G. Prasad
AIP conference proceedings (2024) Vol. 3196, pp. 060002-060002
Closed Access
Foretelling the compressive strength of bamboo using machine learning techniques
Saurabh Dubey, Deepak Gupta, Mainak Mallik
Engineering Computations (2024)
Closed Access
Interpretable machine‐learning models for predicting creep recovery of concrete
Shengqi Mei, Xiaodong Liu, Xingju Wang, et al.
Structural Concrete (2024)
Closed Access
Dynamic perspectives into tropical fruit production: a review of modeling techniques
Daniel Mancero-Castillo, Yoansy García Ortega, Maritza Aguirre-Munizaga, et al.
Frontiers in Agronomy (2024) Vol. 6
Open Access
Data-driven prediction of the shear capacity of ETS-FRP-strengthened beams in the hybrid 2PKT–ML approach
Thai Son Tran, Boonchai Stitmannaithum, Linh Van Hong Bui, et al.
Scientific Reports (2023) Vol. 13, Iss. 1
Open Access | Times Cited: 1
Comparative Analysis of Reinforced Concrete Beam Behaviour: Conventional Model vs. Artificial Neural Network Predictions
Muhammad Mahtab Ahmad, Ayub Elahi, Salim Barbhuiya
Materials (2023) Vol. 16, Iss. 24, pp. 7642-7642
Open Access | Times Cited: 1
Soft Computing for Comprehensive Concrete Strength Prediction – A Comparative Study
S. R. Mugunthan
Journal of Soft Computing Paradigm (2023) Vol. 5, Iss. 4, pp. 417-432
Open Access
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